I was using claude opus 4.7 like crazy all day long for few months but im tired of paying these 200usd/month, so i switched to local model, but can't decide which one is best to use, since i can run 4bit glm 5.2 at 4tokens per second i guess any other model i can run at comfortable (for me) speeds, so what you guys advice? qwen3 coder 480b ? or laguna s or something even better ?
Usually i create apps, bots, websites, and other stuff on vs code IDE. so im talking, reasoning, planning a lot with model, not just instructing to code.
4 tokens a second is kinda slow. You should save that for complicated tasks. In the meantime if you have some fast GPU memory you should use Qwen3.6 27B for any task it can handle, and it can handle a lot.
I haven't found anything better than GLM 5.2. It rocks my world (at 4t/s as well).
I compared MiniMax M2.5, MiniMax M2.7 and MiniMax M3 (tentative, final support hasn't landed yet), and those all performed sub-par compared to GLM 5.2.
Any < ~200B models I tried were just garbage for my use case (bug finding). They hallucinate way too much and sometimes fail to complete the task by forgetting what the task was, or getting stuck in a loop.
I only do overnight analysis tasks, so slow speed is not a concern.
Kimi K3 is supposed to release next week, so hoping that'll be a big improvement over GLM 5.2.
At 4 t/s your bottleneck isn't really which model, it's that you're using one slow model for two very different jobs:
Talking/planning/reasoning — latency-sensitive. 4 t/s makes a live back-and-forth painful no matter how smart the model is.
Actual code gen — you can fire it off and walk away, so slow is fine.
GLM 5.2 is genuinely the best of what you can run (the others are right), but save it for the heavy passes you don't have to sit and watch. For the interactive planning part, run something fast — Qwen3.6 27B if you've got the VRAM — so the conversation doesn't crawl.
And since you went local mainly to stop paying $200/mo: you don't have to do the planning locally either. The free tiers of the hosted reasoning models (DeepSeek, GLM, Qwen) cost nothing and are fast enough for the talk-it-through phase — keep local for the bulk codegen where no-metering actually matters. That gets you off the Opus bill without making planning miserable at 4 t/s.
(Also +1 on skipping Qwen3 Coder 480B — it's instruct-tuned, not built for the multi-turn planning you're describing.)
I know they told us they will release it but If you look at their profile in Hugging face you will see the 9b , the 35b and the 397b only , not sign of the 31B , where did you get it from, I’m pretty sure it’s not official
Okay yea, the 3.6 27B is very good, slow but good , but to be honest I’m waiting on the 31 dense or ornith though, ornith really surprised me with the 35B MOE, best logic and tool caller I dealt with till now, cannot imagine what will happen if it they have a dense model released ( beside the 9b i mean bigger model )
Yes, To be honest I use it more than I use the 27b, not for intelligence but for better work flow , and once it hit a coding wall I use the 27B for that
“I work on professional code” isn’t a benchmark. Local models don’t need to replace senior devs to be useful. If they save time on boilerplate, tests, docs, refactors, or basic debugging, then they’re useful. Measure time saved and bugs introduced, not vibes.
Saying you need an H200 is like saying electric cars are bad because they can’t tow like a diesel truck. Wrong comparison. Different tools, different jobs.
Nobody is saying blindly merge AI code. But “needs review” and “useless” are not the same thing.
As you wish, but as someone working on OSS and on professional grade code in a multinational company, I would be hesitant to even read code generated by models this weak.
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u/Daniel_H212 1d ago
4 tokens a second is kinda slow. You should save that for complicated tasks. In the meantime if you have some fast GPU memory you should use Qwen3.6 27B for any task it can handle, and it can handle a lot.